Search Results
; namely, their role in forcing and coupling with long-lived gravity waves. Among the most dominant drivers of weather variability in the MC is the Madden–Julian oscillation (MJO; Madden and Julian 1972 ). The MJO is a convectively coupled tropical wave that propagates slowly eastward (~5 m s −1 ) through the Indo-Pacific warm pool region, modulating deep overturning motion and moist convection on intraseasonal time scales ( Zhang 2005 ). Yet since the diurnal cycle is the primary rainfall mechanism
; namely, their role in forcing and coupling with long-lived gravity waves. Among the most dominant drivers of weather variability in the MC is the Madden–Julian oscillation (MJO; Madden and Julian 1972 ). The MJO is a convectively coupled tropical wave that propagates slowly eastward (~5 m s −1 ) through the Indo-Pacific warm pool region, modulating deep overturning motion and moist convection on intraseasonal time scales ( Zhang 2005 ). Yet since the diurnal cycle is the primary rainfall mechanism
-propagation speed, and seasonal cycle. Hung et al. (2013) indicated only one GCM was able to simulate the observed eastward propagation of the MJO in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Jiang et al. (2015) showed that only a quarter of the GCMs that participated in the MJO Task Force (MJOTF) ( Moncrieff et al. 2012 ; Waliser et al. 2012 ) and the GEWEX Atmospheric System Study (GASS) ( Petch et al. 2011 ; we refer to this by the abbreviation MJOTF/GASS in this study) could produce
-propagation speed, and seasonal cycle. Hung et al. (2013) indicated only one GCM was able to simulate the observed eastward propagation of the MJO in phase 5 of the Coupled Model Intercomparison Project (CMIP5). Jiang et al. (2015) showed that only a quarter of the GCMs that participated in the MJO Task Force (MJOTF) ( Moncrieff et al. 2012 ; Waliser et al. 2012 ) and the GEWEX Atmospheric System Study (GASS) ( Petch et al. 2011 ; we refer to this by the abbreviation MJOTF/GASS in this study) could produce
maintain consistency with observed intraseasonal variability, spectral nudging toward ERA-Interim was performed for the 12-km domain only for wavelengths longer than 1000 km above the boundary layer, with an inverse nudging time scale of 0.0003 s −1 for all nudged variables. Liu et al. (2012) showed that spectral nudging achieved a better balance between maintaining consistency with large-scale forcing while allowing smaller-scale variance to develop than grid nudging, and Vincent and Hahmann (2015
maintain consistency with observed intraseasonal variability, spectral nudging toward ERA-Interim was performed for the 12-km domain only for wavelengths longer than 1000 km above the boundary layer, with an inverse nudging time scale of 0.0003 s −1 for all nudged variables. Liu et al. (2012) showed that spectral nudging achieved a better balance between maintaining consistency with large-scale forcing while allowing smaller-scale variance to develop than grid nudging, and Vincent and Hahmann (2015
layer” parameterization scheme is based on that of Lock et al. (2000) with the modifications described in Lock (2001) and Brown et al. (2008) . It is a first-order turbulence closure mixing adiabatically conserved heat and moisture variables, momentum, and tracers. For more details of the model physics, readers are referred to Walters et al. (2017) . We examine UM-GA6’s performance in the Atmospheric Model Intercomparison Project (AMIP) runs from 1982 to 2008 with observed SST forcing. The
layer” parameterization scheme is based on that of Lock et al. (2000) with the modifications described in Lock (2001) and Brown et al. (2008) . It is a first-order turbulence closure mixing adiabatically conserved heat and moisture variables, momentum, and tracers. For more details of the model physics, readers are referred to Walters et al. (2017) . We examine UM-GA6’s performance in the Atmospheric Model Intercomparison Project (AMIP) runs from 1982 to 2008 with observed SST forcing. The
conspire to promote nocturnal low-level convergence, moistening, and destabilization, and hence provide explanations for the triggering and maintenance of these MCSs, though not their propagation. Yet in other regions, nocturnal propagating convective systems exist both without low-level jets and without continuous orographic forcing, as exemplified by the many examples of offshore-propagating nocturnal systems: in the Tiwi Islands ( Carbone et al. 2000 ); the Panama Bight region ( Mapes et al. 2003a
conspire to promote nocturnal low-level convergence, moistening, and destabilization, and hence provide explanations for the triggering and maintenance of these MCSs, though not their propagation. Yet in other regions, nocturnal propagating convective systems exist both without low-level jets and without continuous orographic forcing, as exemplified by the many examples of offshore-propagating nocturnal systems: in the Tiwi Islands ( Carbone et al. 2000 ); the Panama Bight region ( Mapes et al. 2003a
represent the wind forcing in the Maluku Channel. Surface drifter trajectories are obtained from the Global Lagrangian Drifter Data of AOML/NOAA from 1979 to 2011 ( http://www.aoml.noaa.gov/envids/gld/dirkrig/parttrk_spatial_temporal.php ). The 2-Minute Gridded Global Relief Data (ETOPO2v2) of the U.S. National Geophysical Data Center are used to calculate the width of the Maluku Channel section ( https://ngdc.noaa.gov/mgg/global/etopo2.html ). The drifters released in the western Pacific Ocean during
represent the wind forcing in the Maluku Channel. Surface drifter trajectories are obtained from the Global Lagrangian Drifter Data of AOML/NOAA from 1979 to 2011 ( http://www.aoml.noaa.gov/envids/gld/dirkrig/parttrk_spatial_temporal.php ). The 2-Minute Gridded Global Relief Data (ETOPO2v2) of the U.S. National Geophysical Data Center are used to calculate the width of the Maluku Channel section ( https://ngdc.noaa.gov/mgg/global/etopo2.html ). The drifters released in the western Pacific Ocean during
hindcast simulation forced by the NCEP–NCAR wind forcing during 2000–11 is used to form the mean velocity. The Global Ocean Forecasting System (GOFS) 3.1 operated by the Center for Ocean–Atmosphere Predictions Studies of Florida State University employs the Hybrid Coordinate Ocean Model (HYCOM) forced by the Navy Global Environmental Model (NAVGEM) atmospheric forcing and the U.S. Navy Coupled Ocean Data Assimilation system ( Cummings 2005 ; Chassignet et al. 2009 ), with a horizontal resolution of 0
hindcast simulation forced by the NCEP–NCAR wind forcing during 2000–11 is used to form the mean velocity. The Global Ocean Forecasting System (GOFS) 3.1 operated by the Center for Ocean–Atmosphere Predictions Studies of Florida State University employs the Hybrid Coordinate Ocean Model (HYCOM) forced by the Navy Global Environmental Model (NAVGEM) atmospheric forcing and the U.S. Navy Coupled Ocean Data Assimilation system ( Cummings 2005 ; Chassignet et al. 2009 ), with a horizontal resolution of 0
layer salinity in the southeastern tropical Indian Ocean is influenced by the annual cycles of the ITF and the Leeuwin Current transports, air–sea freshwater forcing, and eddy fluxes ( Zhang et al. 2016 ). Strong salinity fronts observed within the equatorial region show meridional migration associated with the intertropical convergence zone and meridional ocean currents, which may be modulated interannually by zonal advections of less saline waters from the eastern Indian Ocean related to the ITF
layer salinity in the southeastern tropical Indian Ocean is influenced by the annual cycles of the ITF and the Leeuwin Current transports, air–sea freshwater forcing, and eddy fluxes ( Zhang et al. 2016 ). Strong salinity fronts observed within the equatorial region show meridional migration associated with the intertropical convergence zone and meridional ocean currents, which may be modulated interannually by zonal advections of less saline waters from the eastern Indian Ocean related to the ITF
Zealand downstream. Hence the change in local winds also force some modifications in surface fluxes and wind stress. Any link between ENSO-related variations in the ITF and the Tasman Sea heat waves has been generally assigned to the atmospheric bridge connections. The studies thus far have overlooked the likelihood that there is also a direct ocean connection through the changes in mass and heat transport with the ITF that indeed relate to opposite changes in the East Australian Current region
Zealand downstream. Hence the change in local winds also force some modifications in surface fluxes and wind stress. Any link between ENSO-related variations in the ITF and the Tasman Sea heat waves has been generally assigned to the atmospheric bridge connections. The studies thus far have overlooked the likelihood that there is also a direct ocean connection through the changes in mass and heat transport with the ITF that indeed relate to opposite changes in the East Australian Current region
the Tropical Rainfall Measuring Mission (TRMM; Huffman et al. 2007 ), which provides a continuous rainfall dataset covering the period from 1998 onward and allows direct study on the rainfall impacts of CSs and MJO that provide forcing from outside of the region. The quasistationary Borneo vortex that was studied by Chang et al. (2005a) is a local system that interacts with both cold surges and MJO and always has strong effects on rainfall. Its explicit impacts will be left to future study
the Tropical Rainfall Measuring Mission (TRMM; Huffman et al. 2007 ), which provides a continuous rainfall dataset covering the period from 1998 onward and allows direct study on the rainfall impacts of CSs and MJO that provide forcing from outside of the region. The quasistationary Borneo vortex that was studied by Chang et al. (2005a) is a local system that interacts with both cold surges and MJO and always has strong effects on rainfall. Its explicit impacts will be left to future study
